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Proceedings Paper

Tackling the x-ray cargo inspection challenge using machine learning
Author(s): Nicolas Jaccard; Thomas W. Rogers; Edward J. Morton; Lewis D. Griffin
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Paper Abstract

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection.

Paper Details

Date Published: 12 May 2016
PDF: 13 pages
Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470N (12 May 2016); doi: 10.1117/12.2222765
Show Author Affiliations
Nicolas Jaccard, Univ. College London (United Kingdom)
Thomas W. Rogers, Univ. College London (United Kingdom)
Edward J. Morton, Rapiscan Systems Ltd. (United Kingdom)
Lewis D. Griffin, Univ. College London (United Kingdom)


Published in SPIE Proceedings Vol. 9847:
Anomaly Detection and Imaging with X-Rays (ADIX)
Amit Ashok; Mark A. Neifeld; Michael E. Gehm, Editor(s)

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